MEMS Seminar: Distributed Data Fusion - Neighbors, Rumors and the Art of Collective Knowledge

Mar 22

Wednesday, March 22, 2017

1:30 pm - 2:30 pm
Fitzpatrick Center Schiciano Auditorium Side A

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Professor Mark Campbell

Distributed data fusion is the process whereby a group of agents sense their local environment, communicate with other agents, and collectively try to infer knowledge about a particular process. The applications are many: cooperative robots mapping a room; cooperative UAVs tracking a moving object on the ground or searching for survivors; a distributed formation of space telescopes; a game of hide and seek with kids; a group of people discussing an interesting issue, either in person or on-line. This talk will use a paradigm called Bayesian Distributed Data Fusion (DDF) to explore the challenges, solutions and applications related to a group of agents working together sensing, communicating, and inferring processes in their environment. These challenges include: how to build a scalable solution; how to maintain consistent estimates and avoid rumor propagation; how to handle complex information types (such as semantic human inputs); and how to handle varying network topologies. The theoretical underpinnings of these solutions will be studied, along with a series of applications focused cooperating UAVs, robots, and people.


Mark Campbell is the John A. Mellowes '60 Professor and the S. C. Thomas Sze Director of the Sibley School of Mechanical and Aerospace Engineering at Cornell University. He received his B.S. in Mechanical Engineering from Carnegie Mellon, and his M.S. and Ph.D. in Control and Estimation from MIT. His research interests are in the areas of autonomous systems in air, ground or space; human-robotic information sharing and collaboration; and estimation theory.


Lunch will be served from 1 – 1:30 pm.


Siler, Katherine